Borrowing it
Nothing to install: this file belongs to wzk1015/WorldCupArena. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/wzk1015/WorldCupArena/main/CLAUDE.mdgit clone --depth 1 https://github.com/wzk1015/WorldCupArenaWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/wzk1015/worldcuparena/claude-md)<a href="https://agentmods.dev/instructions/wzk1015/worldcuparena/claude-md"><img src="https://agentmods.dev/badge/instructions/wzk1015/worldcuparena/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/wzk1015/worldcuparena/claude-md"><img src="https://agentmods.dev/badge/instructions/wzk1015/worldcuparena/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00604 | $0.00604 |
| Opus 5 | $0.00302 | $0.00302 |
| Sonnet 5 | $0.00121 | $0.00121 |
| Haiku 4.5 | $0.00060 | $0.00060 |
Grade A, and why
WorldCupArena CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
This file provides project-specific context and instructions to Claude.
Project Goal
The primary goal of this project is to benchmark LLMs and deep-research agents on real-world football prediction. This involves everything from predicting the final score to more tactical details like goal scorers and in-match events.
Key Files
configs/models.yaml: Contains the list of LLMs and agents being tested. New models can be added here.configs/settings.yaml: Defines the two settings (S1 and S2) for running the models. S1 is for non-tool LLMs with injected context, and S2 is for tool-using models/agents with self-search.src/: The main source code for the project, broken down intoingest,runners,graders,pipeline, andleaderboard.docs/usage.md: Provides a step-by-step guide on how to use the project.docs/integration.md: Explains how to add new models to the benchmark.
Development Process
The lifecycle of a fixture is as follows (LEAD = WCA_PREDICT_LEAD_H, default 48h; set in src/pipeline/scheduler.py):
- T-(LEAD+24h): Ingest data (squads, form, news, odds) using
ingest.py. - T-LEAD (default T-48h): Lock the snapshot and run predictions for all models. This is also the anti-leakage information horizon (
lock_at). - T+3h: Ingest the results of the match.
- T+24h: Grade the predictions and build the leaderboard.
Contributions are welcome, especially for new model runners and ingest adapters.
Coding Style
- The project is written in Python.
- Follow the existing code structure when adding new features.
- API keys and other secrets should be stored in a
.envfile locally and in GitHub Actions secrets for CI. - All code should be well-documented.
Running the Website Locally
The project website is a client-side application located in the docs/site/ directory. It works by fetching a data.json file.
To run the full application locally:
- Generate the data file: This script collects all the prediction and result data into the JSON file the website needs.
python3 src/leaderboard/build_site.py
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 49 lines · 604 tokens per session scan A f00681e2a369
WorldCupArena CLAUDE.md is an instructions file published in the GitHub repository wzk1015/WorldCupArena (24 stars, last pushed 1mo ago), licensed MIT. It adds 604 tokens to every session, about $0.0030 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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